Leveraging AI in IBM Cognos Analytics | Watson Demo

Introduction

In today's rapidly evolving business landscape, Artificial Intelligence (AI) is transforming how organizations leverage data. This article delves into the impact of AI, specifically through IBM Cognos Analytics' integration with Watson. Cognos has a rich history steeped in analytics capabilities, consistently enabling users to uncover insights from their data. With the recent release of version 12, this integration has become even more robust and user-friendly.

The Evolution of Cognos Analytics with Watson

IBM Cognos Analytics has been on a journey to empower analysts, shifting the tool from IT professionals to everyday users. The integration of AI, specifically Watson, enhances this approach by providing self-service insights that anyone can utilize. The key feature showcasing this integration is the "Explorations" tool, which has been significantly emphasized in version 12.

Exploring Data Insights: The Role of Explorations

The Explorations feature in Cognos allows users to gain insights by interacting with their data in various ways. Users can start a new Exploration using either a packaged model or by letting Cognos analyze the data set automatically. The AI-driven insights offered by Watson help users identify relationships within the data, highlighting the most relevant variables and how they interact.

One standout functionality within Explorations is the ability to visualize statistical information about the data using predictive strength drivers and correlations. Through these visualizations, users can quickly comprehend what factors most significantly impact different metrics, like order value.

The tree diagram view further enhances the exploratory process, allowing users to drill down into data relationships, modify visualizations, or revert to the foundational data all within a user-friendly interface.

Empowering Users with AI-Driven Assistance

Another noteworthy feature in IBM Cognos Analytics is the AI assistant, which enables users to generate visualizations and insights through natural language queries. Users can access the assistant from any report or dashboard, making it easier than ever to interact with data.

For instance, while working with a coffee sales data module, users can ask the assistant questions about spoilage rates. The AI accurately returns a visualization and can create an entire dashboard with a single click, streamlining the process of data analysis and presentation.

While this AI functionality has existed in earlier versions, version 12 brings enhanced accessibility, allowing users to summon assistance from the welcome page without navigating away from their current context.

Conclusion: The Future of AI in Data Analytics

The integration of AI within IBM Cognos Analytics underscores a trend towards making advanced analytics more accessible. The key message is that while AI enhances data insights, it requires accurate source data for optimal performance. Senturus is equipped to support organizations on their journey to harnessing the capabilities of AI and analytics, aiming for efficiency and self-sufficiency in data handling.

Keywords

• IBM Cognos Analytics
• Watson
• AI Integration
• Explorations Tool
• Natural Language Queries
• Data Insights
• Predictive Strength
• Self-Service Analytics
• User-Friendly Interface

FAQ

1. What is IBM Cognos Analytics?
IBM Cognos Analytics is a data analytics platform that allows users to analyze data, generate reports, and gain insights from various data sources.

2. How does Watson enhance IBM Cognos Analytics?
Watson integrates AI capabilities into Cognos, providing advanced data exploration tools and insights, making analytics more accessible to non-technical users.

3. What is the Explorations feature in Cognos?
Explorations is a tool that enables users to interact with their data dynamically, revealing insights through visualization and statistical analysis.

4. Can users create visualizations using natural language in Cognos?
Yes, users can utilize the AI assistant to ask natural language questions and generate visualizations and dashboards quickly.

5. Why is accurate data important for AI analytics?
Accurate source data is crucial for AI analytics as it ensures the effectiveness of data insights and the reliability of the analysis outcomes.